A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the Köppen–Trewartha (K–T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about $\pm 0.5$ K) with low spatial standard deviation (about $\pm 1.5$ K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below $\pm 50$ % in most domains; however, spatial standard deviation varies from $\pm 30$ % to $\pm 200$ %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing.

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

Radiation feedback plays a crucial role in the process of star formation. In order to simulate the thermodynamic evolution of disks, filaments, and the molecular gas surrounding clusters of young stars, we require an efficient and accurate method for solving the radiation transfer problem. We describe the implementation of a hybrid radiation transport scheme in the adaptive grid-based FLASH general magnetohydrodyanmics code. The hybrid scheme splits the radiative transport problem into a raytracing step and a diffusion step. The raytracer captures the first absorption event, as stars irradiate their environments, while the evolution of the diffuse component of the radiation field is handled by a flux-limited diffusion solver. We demonstrate the accuracy of our method through a variety of benchmark tests including the irradiation of a static disk, subcritical and supercritical radiative shocks, and thermal energy equilibration. We also demonstrate the capability of our method for casting shadows and calculating gas and dust temperatures in the presence of multiple stellar sources. Our method enables radiation-hydrodynamic studies of young stellar objects, protostellar disks, and clustered star formation in magnetized, filamentary environments.

This paper introduces REMO2020, a modernised version of the well-known and widely used REgional climate MOdel (REMO). REMO2020 has undergone fundamental changes in its code structure to provide a more modular, operationally focused design, facilitating the inclusion of new components and updates. Here, we describe the default configuration of REMO2020, which includes the following updates compared to the previous version, REMO2015: (i) the FLake lake model, (ii) a state-of-the-art MACv2-SP aerosol climatology, (iii) a newly developed three-layer snow module, (iv) a prognostic precipitation scheme, (v) an updated time filter, and (vi) a new tuning approach. Additionally, we describe some optional modules that can be activated separately, such as the interactive MOsaic-based VEgetation model iMOVE. REMO2020 outperforms its predecessor REMO2015 with regard to nearly all evaluation metrics used to evaluate simulations of Europe's climate. The persistent warm-temperature bias over central Europe and the cold-temperature bias over northern Europe have been significantly reduced in REMO2020. Similarly, the previously modelled dry bias in central Europe has been nearly eliminated, and the extent of the wet bias in eastern Europe has been reduced. The precipitation distribution in REMO2020 is much more realistic, especially in terms of heavy-precipitation extremes. Statistically, REMO2020 aligns better with long-term measurements than with older versions. Mountainous areas still present a challenge in REMO2020, especially with a higher vertical resolution. In this paper, we demonstrate why REMO2020 will be our new model version for future dynamical downscaling activities.